






'''simple docstring'''
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_											=			1_6
a_											=			3_2





def 			_a(	UpperCamelCase__					:    List[str],						UpperCamelCase__					:    Tuple = 1_6				):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=AutoTokenizer.from_pretrained('''bert-base-cased'''				)
      SCREAMING_SNAKE_CASE__  :					str						=load_dataset('''glue''',						'''mrpc'''				)

      def tokenize_function(UpperCamelCase__					:    str				):
            # max_length=None => use the model max length (it's actually the default)
            SCREAMING_SNAKE_CASE__  :					str						=tokenizer(examples['''sentence1'''],						examples['''sentence2'''],						truncation=A_,						max_length=A_				)
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      # starting with the main process first:
      with accelerator.main_process_first():
            SCREAMING_SNAKE_CASE__  :					List[str]						=datasets.map(
                A_,						batched=A_,						remove_columns=['''idx''', '''sentence1''', '''sentence2'''],						)

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      SCREAMING_SNAKE_CASE__  :					Dict						=tokenized_datasets.rename_column('''label''',						'''labels'''				)

      def collate_fn(UpperCamelCase__					:    Optional[int]				):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            SCREAMING_SNAKE_CASE__  :					str						=1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
            # When using mixed precision we want round multiples of 8/16
            if accelerator.mixed_precision == "fp8":
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=1_6
            elif accelerator.mixed_precision != "no":
                  SCREAMING_SNAKE_CASE__  :					int						=8
            else:
                  SCREAMING_SNAKE_CASE__  :					str						=None

            return tokenizer.pad(
                A_,						padding='''longest''',						max_length=A_,						pad_to_multiple_of=A_,						return_tensors='''pt''',						)

      # Instantiate dataloaders.
      SCREAMING_SNAKE_CASE__  :					List[Any]						=DataLoader(
          tokenized_datasets['''train'''],						shuffle=A_,						collate_fn=A_,						batch_size=A_				)
      SCREAMING_SNAKE_CASE__  :					int						=DataLoader(
          tokenized_datasets['''validation'''],						shuffle=A_,						collate_fn=A_,						batch_size=A_				)

      return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  a_											=			mocked_dataloaders  # noqa: F811





def 			_a(	UpperCamelCase__					:    int,						UpperCamelCase__					:    Union[str, Any]				):




      '''simple docstring'''






      if os.environ.get('''TESTING_MOCKED_DATALOADERS''',						A_				) == "1":
            SCREAMING_SNAKE_CASE__  :					Tuple						=2
      # New Code #
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=int(args.gradient_accumulation_steps				)
      # Initialize accelerator
      SCREAMING_SNAKE_CASE__  :					Tuple						=Accelerator(
          cpu=args.cpu,						mixed_precision=args.mixed_precision,						gradient_accumulation_steps=A_				)
      if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
            raise NotImplementedError(
                '''Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'''				)
      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      SCREAMING_SNAKE_CASE__  :					List[str]						=config['''lr''']
      SCREAMING_SNAKE_CASE__  :					List[str]						=int(config['''num_epochs''']				)
      SCREAMING_SNAKE_CASE__  :					int						=int(config['''seed''']				)
      SCREAMING_SNAKE_CASE__  :					Tuple						=int(config['''batch_size''']				)

      SCREAMING_SNAKE_CASE__  :					List[Any]						=evaluate.load('''glue''',						'''mrpc'''				)

      set_seed(A_				)
      SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Any						=get_dataloaders(A_,						A_				)
      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''',						return_dict=A_				)

      # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
      # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
      # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
      SCREAMING_SNAKE_CASE__  :					Optional[Any]						=model.to(accelerator.device				)

      # Instantiate optimizer
      SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=AdamW(params=model.parameters(),						lr=A_				)

      # Instantiate scheduler
      SCREAMING_SNAKE_CASE__  :					Dict						=get_linear_schedule_with_warmup(
          optimizer=A_,						num_warmup_steps=1_0_0,						num_training_steps=(len(A_				) * num_epochs),						)

      # Prepare everything
      # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
      # prepare method.
      SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Optional[int]						=accelerator.prepare(
          A_,						A_,						A_,						A_,						A_				)

      # Now we train the model
      for epoch in range(A_				):
            model.train()
            for step, batch in enumerate(A_				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  # New code #
                  # We use the new `accumulate` context manager to perform gradient accumulation
                  # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                  with accelerator.accumulate(A_				):
                        SCREAMING_SNAKE_CASE__  :					Optional[Any]						=model(**A_				)
                        SCREAMING_SNAKE_CASE__  :					Dict						=output.loss
                        accelerator.backward(A_				)
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(A_				):
                  # We could avoid this line since we set the accelerator with `device_placement=True`.
                  batch.to(accelerator.device				)
                  with torch.no_grad():
                        SCREAMING_SNAKE_CASE__  :					Optional[int]						=model(**A_				)
                  SCREAMING_SNAKE_CASE__  :					Optional[int]						=outputs.logits.argmax(dim=-1				)
                  SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__  :					Union[str, Any]						=accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
                  metric.add_batch(
                      predictions=A_,						references=A_,						)

            SCREAMING_SNAKE_CASE__  :					Optional[Any]						=metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f"epoch {epoch}:",						A_				)





def 			_a(	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__  :					List[Any]						=argparse.ArgumentParser(description='''Simple example of training script.'''				)
      parser.add_argument(
          '''--mixed_precision''',						type=A_,						default=A_,						choices=['''no''', '''fp16''', '''bf16''', '''fp8'''],						help='''Whether to use mixed precision. Choose'''
          '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
          '''and an Nvidia Ampere GPU.''',						)
      # New Code #
      parser.add_argument(
          '''--gradient_accumulation_steps''',						type=A_,						default=1,						help='''The number of minibatches to be ran before gradients are accumulated.''',						)
      parser.add_argument('''--cpu''',						action='''store_true''',						help='''If passed, will train on the CPU.'''				)
      SCREAMING_SNAKE_CASE__  :					Optional[int]						=parser.parse_args()
      SCREAMING_SNAKE_CASE__  :					Tuple						={'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
      training_function(A_,						A_				)


if __name__ == "__main__":
  main()

import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if gpta_config_file == "":
    __magic_name__								=			GPTaConfig()
  else:
    __magic_name__								=			GPTaConfig.from_json_file(A_   )
  __magic_name__								=			GPTaModel(A_   )

  # Load weights from numpy
  load_tf_weights_in_gpta(A_,	A_,	A_   )

  # Save pytorch-model
  __magic_name__								=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __magic_name__								=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(model.state_dict(),	A_   )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(config.to_json_string()   )


if __name__ == "__main__":
    __lowerCAmelCase     :	List[str]       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--gpt2_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    parser.add_argument(
        '--gpt2_config_file',
        default='',
        type=str,
        help=(
            'An optional config json file corresponding to the pre-trained OpenAI model. \n'
            'This specifies the model architecture.'
        ),
    )
    __lowerCAmelCase     :	Tuple       			=  parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)

